Web page classification based-on a least square support vector machine with latent semantic analysis

doi:10.1109/FSKD.2008.259

CORC > 兰州理工大学 > 兰州理工大学

	Web page classification based-on a least square support vector machine with latent semantic analysis
	Zhang, Yong; Fan, Bin; Xiao, Long-Bin
	2008
会议日期	October 18, 2008 - October 20, 2008
会议地点	Jinan, Shandong, China
关键词	Data mining Fuzzy systems Semantics Support vector machines Websites Document matrices Experimental comparison Latent Semantic Analysis Latent semantics Least square support vector machines Massive data Singular value decompostion Web page classification
卷号	2
DOI	10.1109/FSKD.2008.259
页码	528-532
英文摘要	Chinese web page classification(WPC) has been considered as a hot research area in data mining. In order to effectively classify web pages, we present a web page categorization based on a least square support vector machine(LS-SVM) with latent semantic analysis (LSA) . LSA uses Singular Value Decompostion(SVD) to obtain latent semantic structure of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of web page expression, and make use of summarization algorithm to reduce the noise of web pages. A preliminary experimental comparison is made showing encouraging results. © 2008 IEEE.
会议录	Proceedings - 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008
会议录出版者	IEEE Computer Society
语种	英语
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/116564]
专题	兰州理工大学
作者单位	School of Computer and Communication, Lanzhou University of Tech., Lanzhou 730050, China
推荐引用方式 GB/T 7714	Zhang, Yong,Fan, Bin,Xiao, Long-Bin. Web page classification based-on a least square support vector machine with latent semantic analysis[C]. 见:. Jinan, Shandong, China. October 18, 2008 - October 20, 2008.